Team Cyan 2

Ong Zhen Yu Brandon, Goh Boon Chun, Chng Jun Jie Jeremy, Eisen Reiner Baguilat Perdido, Mageshwaran SO Muthusamy

28 July 2023

Relationship between Safe Drinking Water and Community Well-being

Purpose of this presentation

The purpose of this presentation is to examine the relationship between indicators of good health and well-being, specifically targeting the reduction of deaths and illnesses caused by hazardous chemicals, air, water, soil pollution, and contamination.

The presentation will explore the significance of these indicators and potential strategies for achieving improvements in this area.

Research Questions

  • To what extent does exposure to hazardous chemicals and pollution influence mortality and illness rates?
  • Specifically, what is the relationship between access to safe and affordable drinking water and the proportion of population using safe drinking water services?
  • How does the availability of sanitation and hygiene facilities, particularly targeting women, affect the mortality rate attributed to unsafe water and hygiene?
  • What are the key factors contributing to unintentional poisoning mortality, and how can they be mitigated?

Importance of this research

  • Public health impact
    Addresses hazardous chemicals, pollution, and unsafe water’s effect on mortality and illness rates.

  • Sustainable Development Goals
    Supports Goal 3 by focusing on reducing deaths and illnesses from pollution.

  • Policy implications
    Informs targeted strategies to address health risks.

  • Well-being enhancement
    Contributes to promoting healthier lives and well-being.

UN Sustainable Development Goals

Health and water indicators are related to Goals 3 and 6.

UN Targets Addressed

  • Good Health and Well-being

    • Target 3.9: Reduce the number of deaths and illnesses from hazardous chemicals and air, water and soil pollution and contamination
  • Clean Water and Sanitation
    • Target 6.1: Achieve universal and equitable access to safe and affordable drinking water for all
    • Target 6.2: Achieve adequate and equitable sanitation and hygiene targeting women

Selected Indicators

  • Target 3.9.2 Mortality rate attributed to unsafe water, unsafe sanitation and lack of hygiene (exposure to unsafe Water, Sanitation and Hygiene for All (WASH) services)
  • Target 3.9.3 Mortality rate attributed to unintentional poisoning
  • Target 6.1.1 Proportion of population using safely managed drinking water services
  • Target 6.2.1 Proportion of population using (a) safely managed sanitation services and (b) a hand-washing facility with soap and water

Map: Managed sanitation, hand-washing proportion (6.2.1): All areas

Map: Safely managed sanitation, hand-washing proportion (6.2.1): Urban areas

Map: Safely managed sanitation, hand-washing proportion (6.2.1): Rural areas

Map: Unintentional poisoning mortality rate (3.9.3): Male

Map: Unintentional poisoning mortality rate (3.9.3): Female

Map: Unintentional poisoning mortality rate (3.9.3): Both genders

MR vs Population with basic handwashing facilities

There is a strong negative correlation between Mortality rate and Population with basic handwashing facilities

Mortality rate vs Population with basic handwashing facilities

Mortality rate vs Population with basic handwashing facilities (Normalised)

Map: Safely managed drinking water proportion (6.1.1): All areas

Map: Safely managed drinking water proportion (6.1.1): Urban areas

Map: Safely managed drinking water proportion (6.1.1): Rural areas

Map: Water, sanitation, hygiene mortality rate (3.9.2)

Mortality Rate vs Water Services

Mortality Rate vs Water Services

Mortality Rate vs Water Services (Normalised)

Table of all countries representation

Correlation between all 4 indicators

\(\textit{k}\)-Means Clustering

Considering the four selected Sustainable Development Goals, what is an appropriate number, \(\textit{k}\), of clusters for countries?

Elbow at 3, so we choose k=3

Pairs Plot Highlighting Clusters

  1. Low mortality rate
  1. High sanitation and Low mortality rate
  1. Low sanitation and high mortality rate

Decision Tree

Can the cluster assignment be predicted based on the country’s income group, population size and region according to the 7-class World Bank categorisation?

The decision tree only considered the region variable for classification.

Evaluation of Decision Tree

Relative importance of the predictors for classification are as follows:

  • Region: 18.01
  • Income: 6.49
  • Population size: 1.74

For a single train-test split of [75 25], the model obtained a decent prediction accuracy of 0.7.

However, the accuracy for ten-fold cross validation was low, at 0.58. This is probably because the dataset is too small, and the model is overfitting. Overall, the model is unable to generalize to unseen data.

Summary of Quantitative Results

  • Hygiene factors (handwashing facilities and safe drinking water) have significant negative correlations with mortality rates (per 100,000 population).
  • Access to handwashing facilities shows a strong inverse relationship with mortality rates (correlation coefficient: -0.7) for all areas.
  • Access to safely managed drinking water is strongly negatively correlated with mortality rates (correlation coefficient: -0.87) for all areas

Policy Implications

  • Importance of handwashing facilities and safe drinking water in lowering mortality rates, especially related to unsafe water.
  • Urgency for public health policies to expand access to these essential facilities and services.
  • Valuable insights for policy-making bodies on the significant impact of investments in handwashing and safe water.
  • Overall, these findings highlight the importance of prioritizing these interventions to reduce mortality rates effectively.

End

[1] 78
C:\Users\GOH BOON CHUN\Documents\GitHub\csc3007-cyan2\slides.qmd:290:81: style: [line_length_linter] Lines should not be more than 80 characters.
    main.title = "Population with basic handwashing facilities by Country for 2019"
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      "<br>Population with basic handwashing facilities (%): ", pop_with_basic_handwashing_facilites_2019,
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C:\Users\GOH BOON CHUN\Documents\GitHub\csc3007-cyan2\slides.qmd:406:81: style: [line_length_linter] Lines should not be more than 80 characters.
      "<br>Population with basic handwashing facilities (%): ", pop_with_basic_handwashing_facilites_2019,
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C:\Users\GOH BOON CHUN\Documents\GitHub\csc3007-cyan2\slides.qmd:442:81: style: [line_length_linter] Lines should not be more than 80 characters.
      "<br>Population with basic handwashing facilities (%): ", pop_with_basic_handwashing_facilites_2019,
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C:\Users\GOH BOON CHUN\Documents\GitHub\csc3007-cyan2\slides.qmd:502:81: style: [line_length_linter] Lines should not be more than 80 characters.
      "<br>Mortality Rate Unintentional Poisoning per 100,000: ", unintentional_poisoning_2019,
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C:\Users\GOH BOON CHUN\Documents\GitHub\csc3007-cyan2\slides.qmd:541:81: style: [line_length_linter] Lines should not be more than 80 characters.
      "<br>Mortality Rate Unintentional Poisoning per 100,000: ", unintentional_poisoning_2019,
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C:\Users\GOH BOON CHUN\Documents\GitHub\csc3007-cyan2\slides.qmd:580:81: style: [line_length_linter] Lines should not be more than 80 characters.
      "<br>Mortality Rate Unintentional Poisoning per 100,000: ", unintentional_poisoning_2019,
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C:\Users\GOH BOON CHUN\Documents\GitHub\csc3007-cyan2\slides.qmd:600:81: style: [line_length_linter] Lines should not be more than 80 characters.
# Plots of Indicator between mortality rate and population with basic handwashing facilities
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C:\Users\GOH BOON CHUN\Documents\GitHub\csc3007-cyan2\slides.qmd:645:81: style: [line_length_linter] Lines should not be more than 80 characters.
    title = "Correlation between Mortality Rate and Basic Handwashing Facilities \n for Both Gender",
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C:\Users\GOH BOON CHUN\Documents\GitHub\csc3007-cyan2\slides.qmd:664:81: style: [line_length_linter] Lines should not be more than 80 characters.
  countries_sf$pop_with_basic_handwashing_facilites_2019[countries_sf$area == "All areas"],
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C:\Users\GOH BOON CHUN\Documents\GitHub\csc3007-cyan2\slides.qmd:669:81: style: [line_length_linter] Lines should not be more than 80 characters.
  countries_sf$pop_with_basic_handwashing_facilites_2019[countries_sf$area == "Urban"],
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C:\Users\GOH BOON CHUN\Documents\GitHub\csc3007-cyan2\slides.qmd:674:81: style: [line_length_linter] Lines should not be more than 80 characters.
  countries_sf$pop_with_basic_handwashing_facilites_2019[countries_sf$area == "Rural"],
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C:\Users\GOH BOON CHUN\Documents\GitHub\csc3007-cyan2\slides.qmd:705:81: style: [line_length_linter] Lines should not be more than 80 characters.
stacked_bar_chart <- ggplot(countries_sf, aes(x = unintentional_poisoning_2019, fill = area)) +
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C:\Users\GOH BOON CHUN\Documents\GitHub\csc3007-cyan2\slides.qmd:718:81: style: [line_length_linter] Lines should not be more than 80 characters.
grouped_bar_chart <- ggplot(countries_sf, aes(x = unintentional_poisoning_2019, fill = area)) +
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C:\Users\GOH BOON CHUN\Documents\GitHub\csc3007-cyan2\slides.qmd:733:81: style: [line_length_linter] Lines should not be more than 80 characters.
ggplot_stacked_bar_chart <- ggplotly(stacked_bar_chart, width = 1000, height = 600, autosize = TRUE)
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C:\Users\GOH BOON CHUN\Documents\GitHub\csc3007-cyan2\slides.qmd:734:81: style: [line_length_linter] Lines should not be more than 80 characters.
ggplot_grouped_bar_chart <- ggplotly(grouped_bar_chart, width = 1000, height = 600, autosize = TRUE)
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C:\Users\GOH BOON CHUN\Documents\GitHub\csc3007-cyan2\slides.qmd:745:81: style: [line_length_linter] Lines should not be more than 80 characters.
countries_sf$unintentional_poisoning_2019.positive <- countries_sf$unintentional_poisoning_2019 + abs(min(countries_sf$unintentional_poisoning_2019)) + 0.1
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C:\Users\GOH BOON CHUN\Documents\GitHub\csc3007-cyan2\slides.qmd:747:81: style: [line_length_linter] Lines should not be more than 80 characters.
bc_result <- MASS::boxcox(countries_sf$unintentional_poisoning_2019.positive ~ 1,
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C:\Users\GOH BOON CHUN\Documents\GitHub\csc3007-cyan2\slides.qmd:756:81: style: [line_length_linter] Lines should not be more than 80 characters.
  countries_sf$latest_value.x_bc <- log(countries_sf$unintentional_poisoning_2019.positive)
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C:\Users\GOH BOON CHUN\Documents\GitHub\csc3007-cyan2\slides.qmd:758:81: style: [line_length_linter] Lines should not be more than 80 characters.
  countries_sf$latest_value.x_bc <- (countries_sf$unintentional_poisoning_2019.positive^optimal_lambda - 1) / optimal_lambda
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countries_sf$latest_value.x_bc <- countries_sf$latest_value.x_bc + abs(min_value) + 0.1
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countries_sf$latest_value.x_bc_cat <- cut(countries_sf$latest_value.x_bc_rounded, breaks = 50)
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C:\Users\GOH BOON CHUN\Documents\GitHub\csc3007-cyan2\slides.qmd:776:81: style: [line_length_linter] Lines should not be more than 80 characters.
stacked_bar_chart_bc <- ggplot(countries_sf, aes(x = latest_value.x_bc_rounded, fill = area)) +
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C:\Users\GOH BOON CHUN\Documents\GitHub\csc3007-cyan2\slides.qmd:789:81: style: [line_length_linter] Lines should not be more than 80 characters.
grouped_bar_chart_bc <- ggplot(countries_sf, aes(x = latest_value.x_bc_rounded, fill = area)) +
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C:\Users\GOH BOON CHUN\Documents\GitHub\csc3007-cyan2\slides.qmd:806:81: style: [line_length_linter] Lines should not be more than 80 characters.
plot_loess <- ggplot(countries_sf, aes(x = latest_value.x_bc, y = pop_with_basic_handwashing_facilites_2019)) +
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C:\Users\GOH BOON CHUN\Documents\GitHub\csc3007-cyan2\slides.qmd:835:81: style: [line_length_linter] Lines should not be more than 80 characters.
    title = "Correlation between Mortality Rate and Basic Handwashing Facilities (Normalised) for Both Gender",
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C:\Users\GOH BOON CHUN\Documents\GitHub\csc3007-cyan2\slides.qmd:855:81: style: [line_length_linter] Lines should not be more than 80 characters.
  countries_sf$pop_with_basic_handwashing_facilites_2019[countries_sf$area == "All areas"]
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C:\Users\GOH BOON CHUN\Documents\GitHub\csc3007-cyan2\slides.qmd:860:81: style: [line_length_linter] Lines should not be more than 80 characters.
  countries_sf$pop_with_basic_handwashing_facilites_2019[countries_sf$area == "Urban"]
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C:\Users\GOH BOON CHUN\Documents\GitHub\csc3007-cyan2\slides.qmd:865:81: style: [line_length_linter] Lines should not be more than 80 characters.
  countries_sf$pop_with_basic_handwashing_facilites_2019[countries_sf$area == "Rural"]
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C:\Users\GOH BOON CHUN\Documents\GitHub\csc3007-cyan2\slides.qmd:909:81: style: [line_length_linter] Lines should not be more than 80 characters.
subplot <- subplot(ggplot_stacked_bar_chart, ggplot_grouped_bar_chart, nrows = 1)
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C:\Users\GOH BOON CHUN\Documents\GitHub\csc3007-cyan2\slides.qmd:916:81: style: [line_length_linter] Lines should not be more than 80 characters.
subplot_normalised <- subplot(ggplot_stacked_bar_chart_bc, ggplot_grouped_bar_chart_bc, nrows = 1)
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C:\Users\GOH BOON CHUN\Documents\GitHub\csc3007-cyan2\slides.qmd:1024:81: style: [line_length_linter] Lines should not be more than 80 characters.
countries_sf_water_all <- countries_sf_water %>% filter(!(urbanisation %in% c("Rural", "Urban")))
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countries_sf_water_urban <- countries_sf_water %>% filter(!(urbanisation %in% c("All area", "Urban")))
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C:\Users\GOH BOON CHUN\Documents\GitHub\csc3007-cyan2\slides.qmd:1026:81: style: [line_length_linter] Lines should not be more than 80 characters.
countries_sf_water_rural <- countries_sf_water %>% filter(!(urbanisation %in% c("Urban", "All areas")))
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C:\Users\GOH BOON CHUN\Documents\GitHub\csc3007-cyan2\slides.qmd:1034:1: style: [object_name_linter] Variable and function name style should be snake_case or symbols.
colorPalette <- colorRampPalette(c("#FF6969", "#00A0D6"))
^~~~~~~~~~~~
C:\Users\GOH BOON CHUN\Documents\GitHub\csc3007-cyan2\slides.qmd:1038:1: style: [object_name_linter] Variable and function name style should be snake_case or symbols.
numBreaks <- 6
^~~~~~~~~
C:\Users\GOH BOON CHUN\Documents\GitHub\csc3007-cyan2\slides.qmd:1045:81: style: [line_length_linter] Lines should not be more than 80 characters.
    fillColor = ~ colorNumeric(palette = colors, domain = c(0, 20, 40, 60, 80, 100))(value_2019),
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C:\Users\GOH BOON CHUN\Documents\GitHub\csc3007-cyan2\slides.qmd:1062:81: style: [line_length_linter] Lines should not be more than 80 characters.
      "<br>Proportion of population access to safely managed drinking water service: ",
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C:\Users\GOH BOON CHUN\Documents\GitHub\csc3007-cyan2\slides.qmd:1083:1: style: [object_name_linter] Variable and function name style should be snake_case or symbols.
colorPalette <- colorRampPalette(c("#FF6969", "#00A0D6"))
^~~~~~~~~~~~
C:\Users\GOH BOON CHUN\Documents\GitHub\csc3007-cyan2\slides.qmd:1087:1: style: [object_name_linter] Variable and function name style should be snake_case or symbols.
numBreaks <- 6
^~~~~~~~~
C:\Users\GOH BOON CHUN\Documents\GitHub\csc3007-cyan2\slides.qmd:1114:81: style: [line_length_linter] Lines should not be more than 80 characters.
      "<br>Proportion of population access to safely managed drinking water service: ",
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C:\Users\GOH BOON CHUN\Documents\GitHub\csc3007-cyan2\slides.qmd:1134:1: style: [object_name_linter] Variable and function name style should be snake_case or symbols.
colorPalette <- colorRampPalette(c("#FF6969", "#00A0D6"))
^~~~~~~~~~~~
C:\Users\GOH BOON CHUN\Documents\GitHub\csc3007-cyan2\slides.qmd:1138:1: style: [object_name_linter] Variable and function name style should be snake_case or symbols.
numBreaks <- 6
^~~~~~~~~
C:\Users\GOH BOON CHUN\Documents\GitHub\csc3007-cyan2\slides.qmd:1145:81: style: [line_length_linter] Lines should not be more than 80 characters.
    fillColor = ~ colorNumeric(palette = colors, domain = c(0, 20, 40, 60, 80, 100))(value_2019),
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C:\Users\GOH BOON CHUN\Documents\GitHub\csc3007-cyan2\slides.qmd:1162:81: style: [line_length_linter] Lines should not be more than 80 characters.
      "<br>Proportion of population access to safely managed drinking water service: ",
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C:\Users\GOH BOON CHUN\Documents\GitHub\csc3007-cyan2\slides.qmd:1182:1: style: [object_name_linter] Variable and function name style should be snake_case or symbols.
colorPalette <- colorRampPalette(c("#FFFFDF", "#FFEEAA", "#FFBB55", "#FF7700", "#FF4400"))
^~~~~~~~~~~~
C:\Users\GOH BOON CHUN\Documents\GitHub\csc3007-cyan2\slides.qmd:1182:81: style: [line_length_linter] Lines should not be more than 80 characters.
colorPalette <- colorRampPalette(c("#FFFFDF", "#FFEEAA", "#FFBB55", "#FF7700", "#FF4400"))
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C:\Users\GOH BOON CHUN\Documents\GitHub\csc3007-cyan2\slides.qmd:1186:1: style: [object_name_linter] Variable and function name style should be snake_case or symbols.
numBreaks <- 6
^~~~~~~~~
C:\Users\GOH BOON CHUN\Documents\GitHub\csc3007-cyan2\slides.qmd:1193:81: style: [line_length_linter] Lines should not be more than 80 characters.
    fillColor = ~ colorNumeric(palette = colors, domain = c(0, 20, 40, 60, 80, 100))(mortality_rate_unsafe_water),
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C:\Users\GOH BOON CHUN\Documents\GitHub\csc3007-cyan2\slides.qmd:1210:81: style: [line_length_linter] Lines should not be more than 80 characters.
      "<br>Mortality Rate from Unsafe Sanitation and Water per 100,000: ", mortality_rate_unsafe_water
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merged_data <- merge(mortality_rate_unsafe_water, proportion_of_safe_water, by = c("geoAreaCode", "geoAreaName"))
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merged_data <- merged_data[!is.na(merged_data$value_2019), ] # remove NA values for 2019
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stacked_bar_chart <- ggplot(merged_data, aes(x = latest_value.x, fill = location_desc)) +
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C:\Users\GOH BOON CHUN\Documents\GitHub\csc3007-cyan2\slides.qmd:1340:81: style: [line_length_linter] Lines should not be more than 80 characters.
grouped_bar_chart <- ggplot(merged_data, aes(x = latest_value.x, fill = location_desc)) +
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merged_data_all <- subset(merged_data, location_desc == "All areas") # change to rural or urban if you want
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merged_data$latest_value.x_positive <- merged_data$latest_value.x + abs(min(merged_data$latest_value.x)) + 0.1
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  merged_data$latest_value.x_bc <- (merged_data$latest_value.x_positive^optimal_lambda - 1) / optimal_lambda
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merged_data$latest_value.x_bc <- merged_data$latest_value.x_bc + abs(min_value) + 0.1
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merged_data$latest_value.x_bc_cat <- cut(merged_data$latest_value.x_bc_rounded, breaks = 50)
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C:\Users\GOH BOON CHUN\Documents\GitHub\csc3007-cyan2\slides.qmd:1414:81: style: [line_length_linter] Lines should not be more than 80 characters.
stacked_bar_chart_bc <- ggplot(merged_data, aes(x = latest_value.x_bc_rounded, fill = location_desc)) +
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C:\Users\GOH BOON CHUN\Documents\GitHub\csc3007-cyan2\slides.qmd:1427:81: style: [line_length_linter] Lines should not be more than 80 characters.
grouped_bar_chart_bc <- ggplot(merged_data, aes(x = latest_value.x_bc_rounded, fill = location_desc)) +
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C:\Users\GOH BOON CHUN\Documents\GitHub\csc3007-cyan2\slides.qmd:1472:81: style: [line_length_linter] Lines should not be more than 80 characters.
    title = "Correlation between Transformed Mortality Rate and Water Services (Normalised)",
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C:\Users\GOH BOON CHUN\Documents\GitHub\csc3007-cyan2\slides.qmd:1544:81: style: [line_length_linter] Lines should not be more than 80 characters.
subplot <- subplot(ggplot_stacked_bar_chart, ggplot_grouped_bar_chart, nrows = 1)
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C:\Users\GOH BOON CHUN\Documents\GitHub\csc3007-cyan2\slides.qmd:1551:81: style: [line_length_linter] Lines should not be more than 80 characters.
subplot_normalised <- subplot(ggplot_stacked_bar_chart_bc, ggplot_grouped_bar_chart_bc, nrows = 1)
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C:\Users\GOH BOON CHUN\Documents\GitHub\csc3007-cyan2\slides.qmd:1561:81: style: [line_length_linter] Lines should not be more than 80 characters.
mr_unsafe_water_indicators <- read_csv("data/indicator_3.9.2.csv", show_col_types = FALSE)
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C:\Users\GOH BOON CHUN\Documents\GitHub\csc3007-cyan2\slides.qmd:1573:81: style: [line_length_linter] Lines should not be more than 80 characters.
# proportion-of-population-with-basic-handwashing-facilities-on-premises-by-urban-rural-percent
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C:\Users\GOH BOON CHUN\Documents\GitHub\csc3007-cyan2\slides.qmd:1634:81: style: [line_length_linter] Lines should not be more than 80 characters.
#| label: select specific columns for population with basic handwashing facilities
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C:\Users\GOH BOON CHUN\Documents\GitHub\csc3007-cyan2\slides.qmd:1760:81: style: [line_length_linter] Lines should not be more than 80 characters.
countries_tb_subset <- dplyr::select(countries_tb, MR_poisoning, MR_unsafe_water, handwash_access, sanitation_access, urbanisation, country.x)
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countries_tb_subset_corr4 <- dplyr::select(countries_tb_subset, MR_unsafe_water, MR_poisoning, handwash_access, sanitation_access, urbanisation, country.x)
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countries_tb_subset_corr4$Urbanisation <- as.factor(countries_tb_subset_corr4$Urbanisation)
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countries_tb_subset_corr4 <- dplyr::select(countries_tb_subset, MR_unsafe_water, MR_poisoning, handwash_access, sanitation_access, urbanisation)
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countries_tb_subset_corr4$Urbanisation <- as.factor(countries_tb_subset_corr4$Urbanisation)
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color_mapping <- c("All areas" = "red", "Urban" = "blue", "Rural" = "green") # Adjust to your actual factor levels and desired colors
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  mutate(code = countrycode(country, origin = "country.name", destination = "wb"))
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C:\Users\GOH BOON CHUN\Documents\GitHub\csc3007-cyan2\slides.qmd:2007:25: warning: [seq_linter] 1:nrow(...) is likely to be wrong in the empty edge case. Use seq_len(nrow(...)) instead.
shuffle_index <- sample(1:nrow(dataset))
                        ^~~~~~~~~~~~~~~
C:\Users\GOH BOON CHUN\Documents\GitHub\csc3007-cyan2\slides.qmd:2068:13: warning: [seq_linter] 1:length(...) is likely to be wrong in the empty edge case. Use seq_along(...) instead.
  for (i in 1:length(labs)) {
            ^~~~~~~~~~~~~~
C:\Users\GOH BOON CHUN\Documents\GitHub\csc3007-cyan2\slides.qmd:2108:1: style: [object_name_linter] Variable and function name style should be snake_case or symbols.
confMatrix <- table(prediction, test$cluster)
^~~~~~~~~~
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# tune_grid = expand.grid(cp=c(0.0001)) # complexity parameter (size of tree)
  ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~